数学科学学院学术报告
Stable image restoration by TV type methods
报告人姓名: 谌稳固
（北京应用物理与计算数学研究所）
报告时间：2023年11月24日 星期五 下午15：00-16：00
报告地点：沙河校区E404
报告摘要：Some new TV type minimization models are introduced to investigate robust image recovery from a certain number of noisy measurements by the proposed TV type minimization models. Error bounds of robust image recovery from compressed measurements via the proposed minimization models are established, and the RIP based condition is improved compared with total variation (TV) minimization. Numerical results of image reconstruction demonstrate our theoretical results and illustrate the efficiency of the proposed TV type minimization models among state of-the-art methods.
报告人简介：北京应用物理与计算数学研究所研究员，博士生导师，主要从事调和分析、压缩感知、机器学习、大数据分析的理论及应用研究，在IEEE Transactions on Information Theory, Applied and Computational Harmonic Analysis，Inverse Problems, SIAM Journal on Imaging Sciences, Journal of Machine Learning等学术期刊发表科研论文70余篇。
邀请人：谢家新
